Cue onset dummy coding
Table of Contents
Cue onset dummy coding
Pain > (Vicarious & Cognitive)
Pain > VC :: load dataset
Pain > VC :: check data coverage
Pain > VC :: Plot diagnostics, before l2norm
Pain > VC :: run robfit
Pain > VC :: remove outliers based on plot
Pain > VC :: plot diagnostics, after l2norm
Pain > VC :: ttest
Pain > VC :: Neurosynth similarity
Vicarious > (Pain & Cognitive)
Vicarious > PC :: load dataset
Vicarious > PC :: check data coverage
Vicarious > PC :: Plot diagnostics, before l2norm
Vicarious > PC :: run robfit
Vicarious > PC :: remove outliers based on plot
Vicarious > PC :: plot diagnostics, after l2norm
Vicarious > PC :: ttest
Vicarious > PC :: Neurosynth similarity
Cognitive > (Pain & Vicarious)
Cognitive > PV :: load dataset
Cognitive > PV :: check data coverage
Cognitive > PV :: Plot diagnostics, before l2norm
Cognitive > PV :: run robfit
Cognitive > PV :: remove outliers based on plot
Cognitive > PV:: plot diagnostics, after l2norm
Cognitive > PV :: ttest
Cognitive > PV :: Neurosynth similarity
Pain > (Vicarious & Cognitive)
Pain > VC :: load dataset
clear all;
close all;
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0010.nii'));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
'stim_P', 'stim_V', 'stim_C', 'stim_G',...
'motor', ...
'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};
Pain > VC :: check data coverage
m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Pain > VC :: Plot diagnostics, before l2norm
drawnow; snapnow
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
Pain > VC :: run robfit
set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;
Pain > VC :: remove outliers based on plot
con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
disp(n);
Pain > VC :: plot diagnostics, after l2norm
imgs2 = con.rescale('l2norm_images');
Pain > VC :: ttest
t = ttest(imgs2);
orthviews(t);
drawnow, snapnow;
fdr_t = threshold(t, .05, 'fdr');
orthviews(fdr_t);
drawnow, snapnow;
fdr_t = threshold(t, .001, 'fdr');
orthviews(fdr_t);
drawnow, snapnow;
create_figure('montage'); axis off;
montage(fdr_t);
drawnow, snapnow;
Pain > VC :: Neurosynth similarity
[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Pain > VC :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
axis image
subplot(1, 3, 2)
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
clear csim
for i = 1:3
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
subplot(1, 3, 3)
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
Vicarious > (Pain & Cognitive)
Vicarious > PC :: load dataset
clear all;
close all;
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0011.nii'));
spm('Defaults','fMRI');
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
'stim_P', 'stim_V', 'stim_C', 'stim_G',...
'motor', ...
'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};
Vicarious > PC :: check data coverage
m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
Vicarious > PC :: Plot diagnostics, before l2norm
drawnow; snapnow;
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
Vicarious > PC :: run robfit
set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;
Vicarious > PC :: remove outliers based on plot
con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
disp(n);
Vicarious > PC :: plot diagnostics, after l2norm
imgs2 = con.rescale('l2norm_images');
Vicarious > PC :: ttest
t = ttest(imgs2);
orthviews(t);
drawnow, snapnow;
fdr_t = threshold(t, .05, 'fdr');
orthviews(fdr_t);
drawnow, snapnow;
create_figure('montage'); axis off;
montage(fdr_t);
drawnow, snapnow;
Vicarious > PC :: Neurosynth similarity
[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Vicarious > PC :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
axis image
subplot(1, 3, 2)
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
clear csim
for i = 1:3
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
subplot(1, 3, 3)
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
Cognitive > (Pain & Vicarious)
Cognitive > PV :: load dataset
clear all;
close all;
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0012.nii'));
spm('Defaults','fMRI');
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
'stim_P', 'stim_V', 'stim_C', 'stim_G',...
'motor', ...
'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};
Cognitive > PV :: check data coverage
m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Cognitive > PV :: Plot diagnostics, before l2norm
drawnow; snapnow;
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
Cognitive > PV :: run robfit
set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;
Cognitive > PV :: remove outliers based on plot
con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
disp(n);
Cognitive > PV:: plot diagnostics, after l2norm
imgs2 = con.rescale('l2norm_images');
Cognitive > PV :: ttest
t = ttest(imgs2);
orthviews(t);
drawnow, snapnow;
fdr_t = threshold(t, .05, 'fdr');
orthviews(fdr_t);
drawnow, snapnow;
create_figure('montage'); axis off;
montage(fdr_t);
drawnow, snapnow;
Cognitive > PV :: Neurosynth similarity
[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Cognitive > PV :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
axis image
subplot(1, 3, 2)
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
clear csim
for i = 1:3
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
subplot(1, 3, 3)
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
drawnow, snapnow;
% save html
pubdir = pwd;
pubfilename = 's03_PVC_cue_dummy_ROI.mlx';
p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
'format', 'html', 'outputDir', pubdir, ...
'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
htmlfile = publish(pubfilename, p);